Two-dimensional (2-D) and three-dimensional (3-D) visualization products for providing medical images can employ rendering techniques to create images from stored electronic data files. The data input used to create the image renderings can be a stack of image slices from a desired imaging modality, for example, a Computed Tomography (CT) or Magnetic Resonance (MR) modality. The visualization can convert the image data into an image volume to create renderings that can be displayed on a workstation display.
Slice-by-slice viewing of medical data may be increasingly difficult for the large data sets now provided by imaging modalities, raising issues of information and data overload and clinical feasibility with current radiology staffing levels. See, e.g., Addressing the Coming Radiology Crisis: The Society for Computer Applications in Radiology Transforming the Radiological Interpretation Process(TRIP™) Initiative, Andriole et al., at URL scarnet.net/trip/pdf/TRIP_White_Paper.pdf (November 2003). In some modalities, patient data sets can have large volumes, such as greater than 1 gigabyte, and can even commonly exceed 10's or 100's of gigabytes, hence terabytes of data in a patient multi-dimensional data set is becoming more common.
The diagnostic task of a clinician such as a radiologist can vary patient to patient and, accordingly so can the desired renderings or views of the medical images of the patient. In some visualization systems, a physician uses an interactive workstation that has a data retrieval interface that obtains the medical data for medical image renderings from electronic volume data sets to generate desired medical representations. Image visualizations using the multi-dimensional image data can be carried out using any suitable system such as, for example, PACS (Picture Archiving and Communication System). PACS is a system that receives images from the imaging modalities, stores the data in archives, and distributes the data to radiologists and clinicians for viewing.
Unfortunately, the size of medical volume data sets can inhibit rapid visualization times, particularly with a resolution sufficient for diagnostic purposes. In some cases, interactive image generation of multi-resolution representations may not be feasible using conventional processing techniques even with the use of fast graphic hardware. Two common methods for creating multi-resolution representations are well known to those of skill in the art as a straightforward sub-sampling and a wavelet-based decomposition. See Kim et al., An Efficient Data Format For Lossless Compression and It's application to Interactive Viewing, 0-7803-3258-X/96, IEEE (1996) and Hashimoto et al., Hierarchical Structure for Data Transmission of Volumetric Medical Images Using Three-Dimensional Wavelet Transform, 0-7803-7324-3/02, IEEE, pp. 1399-1403 (2002). It is also known to employ a lower resolution to reduce data, for example, a “level-of-detail” (LoD) is a very well known term within visualization. For a general description of different data handling techniques including LoD, ad hoc data organizations, approximation techniques, subsampling, and multiresolution representation, see Cignoni et al., Multi-resolution Representation and Visualization of Volume Data, 1077-2626/97, IEEE (1997) (proposing the use of tetrahedral meshes to represent and visualize scalar volume data at multiple resolution).
Due to limits in conventional visualization pipeline resources, three-dimensional (3D) viewing or other interactive viewing can present with different quality levels, such as for example, where a fast, but low quality rendering is used during interaction, such as rotation, zoom, and the like. One reason for the lower quality is that full quality rendering typically can take an undue amount of time to compute such that the response time to a user's action/input (rotate, zoom, etc. . . . ) can be too long such that the user may not experience a “real-time” control of the visualization. Also, where speed is maintained at the expense of quality level, some users complain that visualization is disturbed when quality level changes during interaction. For example, a user typically desires to be able to interact with images such as to rotate a 3D object back and forth and the visual effect (diagnostic value) of the rotation can be reduced or even lost if the feature in focus has a significantly lower quality during rotations.